Note that there might be more than one such pair that are compatible with R. For example, for figure 1 step (2), (w 2 , P), (w 3 , NN), (w 4 , NN), (w 5 , AD) and (w 6 , VV) are all compatible with the gold tag sequence.

at the beginning of each step, a compatible set of (t, l) pairs are initialized and  (Goldberg and Elhadad, 2010).


An overview of the architecture can be found in Figure 4.

in our model, the reading behavior of a subject reading a document is defined as o, which represents the distribution of attention (eye-movements) on every linguistic feature.


spending different amount of time on the documents, and to account for rare or too-frequent linguistic features that have a different amount of total pixel intensity in the image.

the intensity was adjusted in such a way that 1% of the highest and the lowest non-zero pixel values were saturated (i.e.


Studying the variability of data is of special interest because patterns in variance are usually good signals to be considered to discriminate between subjects.

principal Component Analysis (pCA) (Jolliffe, 2002) will be used to obtain the directions in the feature space where the covariance matrix of the observations varies the most, as given by the eigen-vectors associated to the highest absolute eigen-values.


gloss disambiguation), as it has been done in the WordNet 2/eXtendend WordNet project (Harabagiu et al., 1999;Mihalcea and Moldovan, 2001), is very similar to the disambiguation of semantic relations.

many of the features defined in Section 3.1 are similar to those proposed by Moldovan and Novischi (2004).


The same applies to the disambiguation of relations when choosing the first target sense.

we introduce a feature f idx := j that is set to the index of the target sense t j .


(2008b) used only the 80 pairs that were covered by all three similarity metrics they tried.


We had the intuition that the optimal number of expansions may depend on the part of speech of the word to be disambiguated, and perhaps also on the parts of speech of the words being expanded.

we parameterized our expansion procedure such that the part of speech of the target word determined the number of expansions, and also whether all words were expanded or only those of a certain part of speech.


While this is related to our approach of modeling agreeing and disagreeing debate posts, online debate forums (e.g., Volconco.com) are textual as opposed to conversational speeches.

durational and structural features used in the prior works (e.g., time taken by a speaker, speech rate, speaker overlap, votes, etc.)


Formally, for a given style dimension, , we can measure stylistic cohesion on using the following probabilistic expression: where , denote the event that debate posts , respectively exhibit style dimension .

statistically, if the former probability expression in Eq.


A lemmatizer is used to reduce each word to its base form or lemma.

"play", "played", "plays", "playing" are reduced to its lemma "play".


However, most of the popular videos in Youtube can be attributed to more than one genre.

to allow multiple tags to be assigned to a video, a thesholding is done and the prediction is modified as: If the score of the video for any genre is greater than the average score of all the genres, then it is assigned as a possible tag for the video.


There are but modest amounts of data that have been annotated, and structured prediction views each document as a single instance.


In case no reduce operation is triggered then a shift is automatically performed.

only the reduce operations need to be learned.


But of course adults adjust CDS as children get older, making it more and more complex.

one argument claims that the only way children are able to learn language is through the graded simplification made by adults to the input that the child receives.


While we focus on Amharic verb affix segmentation and morphology learning, our goal is a general-purpose ILP morphology learner that automatically segments affixes based on previous knowledge during the learning process.

we seek background knowledge that is plausible across languages that can be combined with language-specific examples and intelligent ways of presenting examples to yield rule hypotheses that generalize to new examples in the language.


This means that, as illustrated in Figure 1, the internal structure of a noun compound is left unanalyzed because it is contained in a bunsetsu phrase.

noun compound analysis has a complementary relationship with full-sentence dependency parsing.


For c SPAN ( i S k ), we do not consider a noun compound nested in a longer noun compound because they cannot be identified confidently.

web-derived spans are clean even though we exploit the whole data.


And at this dimensionality setting SVD and NNSE seem similarly successful.

in the subsequent analyses we concentrate on the combined SVD and combined NNSE models.


Next, we create a set consisting of the top 5 words from this ranked list, and also one word from the bottom half of this list, which is also present in the top 10th percentile of some other column in A.


Ontology reuse and projection is still a very difficult and unresolved issue.

when general ontologies like SUMO (Pease and Niles, 2001) are insufficient, or are not available in a particular language, a new dedicated ontology has to be created.


The derivation probabilities used in our proposed model are estimated from a large monolingual corpus for each language.

we do not require a large parallel corpus.


We use the notation e to represent the combination of e C and {d e C }, and f for f C and {d f C }.

〈e, f 〉 contains 〈e C , f C 〉, {d e C } and {d f C }.


For 〈change, 〉, there are two derivations from " (change)"; " (by)" and " (light verb)".

we need to calculate two monolingual derivation probabilities in addition to the bilingual generation probability.


The vocabulary size of content words is much larger than that of function words.

the number of derivation patterns from function words is quite large, causing their probabilities to be very small.


This can be achieved by subtracting from the precision component of BLEU+1 the score for that component when there are no matches.

the "grounded" precision component of Given the above discussion, we propose the following fixes for the sentence-level BLEU+1: • BLEU, PC-unsmoothed.


Each time PRO samples, selects, and accepts a pair of hypotheses to use for training its classifier, we immediately update the pseudo-corpus by adding to it the sufficient statistics for the higher-scoring sentence, i.e., the positive example, in the pair.

while the pseudo-corpus is initialized with the statistics from previous iterations, it soon gets dominated by statistics for the positive examples that are being accepted as training sentence pairs at the current iteration, since there are more of them than there are one-best hypotheses from previous iterations.


As a result, the optimizer and the final scorer would most certainly see different tokenizations, and more crucially, this could affect the length ratios and thus the brevity penalty.

we report BLEU scores calculated using the multi-bleu scoring tool, which uses the supplied tokenization and does no retokenization; we supplied it with references that were tokenized and truecased using the same models that were used for training and tuning.


Contrary to the suspicion tree shown in Figure 4, this list fails to highlight the main culprits and the relations between the various suspicious forms.

the 5 main distinct suspects identified by the right frontier of the suspicion tree appears as 1st, 3rd, 15th, 19th and 28th in the ranked list.


For larger dependency trees however, improvement is more limited and other error cases becomes visible.

the failure rate is reduced by 10.4 points for NP-ALL (NPs from minimum size 1 to maximum size 91 with the average size 4); and by 10.9 points for S-ALL (sentences from minimum size 1 to maximum size 134 with the average size 22).


As mentioned in Section 2, polarity filtering (Gardent and Kow, 2005) eliminates from the search space those sets of rules which cover the input but cannot possibly lead to a valid derivation either because a substitution node cannot be filled or because a root node fails to have a matching substitution site 4 While (Gardent and Kow, 2005) applies polarity filtering to the initial search space (the set of rules selected by all literals in the input), we apply polarity filtering to each local tree while going up the input tree.

this filtering will weed out all combinations of mother rules and completed immediate daughter rules which cannot possibly yield a complete tree either because some daughter rule cannot be used or because some requirement of the mother rule cannot be satisfied.


The model restricts itself to trees that can be obtained only by permuting child nodes of any of its non-terminal constituents, and it makes a simplifying assumption that the children of a node are permuted independently of any other node in the tree.

the overall probability simplifies to a product of constituent-level permutations: where n denotes a node, I(S) the set of all non-terminal (interior) nodes of the tree, d n are the children of the node n, and π is the permutation function.


If a node does not have a verb descendant we represent the entire subtree by the constituent label of the node.

the parse tree in Figure 2 gets transformed to the following being passed as input to the reordering model: NP-SBJ can read NP PP-TMP.


Specific to the German→English direction we added an extra feature template that fires if word w m and w n are verbs and there is no verb between positions m and n. We do not have a suitable German parser.

as an alternative method to the TSP-based method (in the German→English direction only) we consider reordering using hand-crafted rules.


Thus, the a relation A contains B is defined over objects A and B, which are themselves structures on their own right.

it is reasonable that A and B would be brought to awareness (reified) before the relation of containment.


We note here that while we claim a low degree of domain dependence, had the video been 3D or the agents been humans, it would have imposed considerable difficulties.

to an extent, the perceptual analysis is limited to this kind of domain, but nonetheless, there is nothing specific to the scene.


In our case, however, since the learner is presented with only the visual stream and is not in the presence of the speaker, attention is mediated by visual saliency alone, and not by cues received from the speaker's gaze.

to simulate gaze-based visual attention, we follow the assumption of Perceptual Theory of Mind (Mukerjee and Sarkar, 2007), that the salient features that our cognitive agent discovers through image processing, would also be salient for the speaker involved in the associated commentaries, letting us correlate the visual and linguistic elements coherently.


The next time a similar expression is encountered, the new semantics are compared with the previous mental model so it can be extended.

if our system here, which knows "in the box", now encounters "in the basket" and "in the room", it would gradually be able to generalize the argument of "in the" to the concept of container (and would eventually reject "in the banana", say).


It extends keyphrase extraction (which is typically performed on a single article or document) to the broader setting of extracting all concepts from a book or collection of papers.

index terms tend to be more numerous than keyphrases (the index of a textbook may list hundreds of index terms).


While any of these sentences could be selected into a summary due to similar content words, the corresponding model summary has only the most updated information (12,000 people).

the evaluation scores are dropped although the summarizer picks an informative sentence.


Usage of Chinese word may serve with multi POS (Part-of-Speech) tags.

the chunker is puzzled by those multi-used words.


A number of different word alignment heuristics are implemented; in the end, growdiag-final heuristic shows better performance than others on our aligned corpus.

wA method with grow-diag-final heuristic is regarded as a baseline method.


It is proved that modification to automatic Tibetan BaseNP candidate under partial correspondence turns out to be effective than pure manual translation.

the best overall performance will be regarded as baseline method in next subsection.


We define the Coverage, quasi-precision and precision as metrics to evaluate the experimental results.

the best one (SII method) achieves 1.0, 0.8821 and 0.5283 respectively on the test corpus.


Formula (4.12) and (4.13) give the expression of Chinese and Tibetan sentence as a word sequence.

sP can be expressed as words sequence form in formula (4.14) as below: Then, let's define sentence sequence intersection.


Rule-based NER systems rely on handcrafted grammatical rules written by linguists.

any maintenance applied to rule-based systems is labour-intensive and time consuming especially if linguists with the required knowledge and background are not available.


The main characteristics of Arabic that pose non-trivial challenges for NER task are as follows: • No Capitalization: Capitalization is not a feature of Arabic script unlike the European languages where an NE usually begins with a capital letter.

the usage of the capitalization feature is not an option in Arabic NER.


The Arabic gazetteers are rare as well and limited in size.

researchers tend to build their own Arabic linguistic resources in order to train and evaluate Arabic NER systems.


The maintenance of rule-based systems is not a straightforward process since experienced linguists need to be available to provide the system with the proper adjustments (Petasis et al., 2001).

any adjustment to such systems is labour intensive and time consuming.


Arabic Treebank Part1 v. 2.0 dataset (Maamouri et al., 2003) has no NE annotations and originally designed to support POS tagging in Arabic NLP.

in this research, the ATB dataset has been manually annotated in order to support the Arabic NER task.


It is worth noting that the results of the proposed hybrid system is very close to the results of the rule-based component when it comes to the numerical and temporal expressions, and the two approaches achieve the same results in recognizing NEs of the 3 rd group.

the hybrid approach proves its suitability for the recognition of the three groups of NEs.


In this study, we express the aggregation in a linear combining method and the combined ranking function can be denoted as where w k is the weight assigned to the kth individual summarization method.

to learn the combination weights w k (k = 1, 2, ..., K) is pivotal in the aggregation method.


ROUGE (Lin and Hovy, 2003) (Recall Oriented Understudy for Gisting Evaluation) is widely applied for summarization evaluation by DUC.

we use the ROUGE toolkit 3 to evaluate the summarization results.


In natural language text however, many concepts share the same name and one concept may be referenced by different names.

a search based on pure string matching often yields many irrelevant results, such as a web page on Superman's hometown when indeed the user seeked information on the physicist Metropolis.


According to Ratinov et al., both Precision and Recall for pr ed BOT are 0.66.

the nil prediction c 0 for Taiwan is not counted as a false positive, since we already observe History of China as a false positive, with two true positives from People's Rep. of China and Jiangsu resulting in P = 0.66.


Although we are quite sure that some of the mappings in A Ψ are irrelevant such as nsubj-assmod, for others it's difficult to determine their relevancy with BLC from the linguistic perspective, just as we are not sure whether there are other useful mappings missing in M Ψ .

we adopt an ablation testing strategy to progressively remove those mappings whose removal lead to better performance using the development lexicon L d .


That is, different mappings should have different weights to exploit such difference.

formula (5) can be recast as follows: In this paper, we propose a simple perceptron algorithm to optimize those weights for different mappings using the development lexicon.


With the AverageFrequency PreFilter the threshold is set to 1: where f i is the frequency of the ith collocation/pair and n is the number of collocations extracted.

the AverageFrequency PreFilter filters away all collocations with f below the mean X .


The rationale behind this step is to retrieve all possible intervening patterns between word A and word B.

for each word pair AB, the intervening patterns are collected in a data structure that contains lemma, token and POS for each position, including A and B.


There the task was searching for collocates on the basis of a given list of words.

the vector was built in order to determine the position of any word W with respect to a word from the list.


Thus the vector was built in order to determine the position of any word W with respect to a word from the list.

in Smadja's approach a high standard deviation indicates randomness, and therefore low association strength between the two words.


Given that it is infrequent for MWEs from the same collocation (same first and last word) to be present in the gold standard, extracting more than one pattern per collocation, as SIGMA does, is penalizing for the precision with respect to our gold standard.

still, a quick manual check of the false positives reveals that most of the extracted patterns are actually correct, in that they are variants of the first pattern, such as: if a more flexible comparison is applied, such as allowing for edit distance (Damerau, 1964) up to 3 between the strings, these variants are recognized as true positives and for sIGMA improves by 10 points, as is shown in the fourth column of table 2.


Furthermore, individual clusterings for a range of α values, were all of similar quality.

we cannot expect a more informed prior over α to lead to better performance 3 .


Our DPMM algorithm with this feature representation is very slow due to the high values of |M | (between 2299 and 5559 for our experimental corpora).

we used a different representation (Section 5).


We also want to emphasize that an exhaustive evaluation of the performance of these models on translation tasks would result in a combinatoric explosion of models where chunking and splitting could be applied at various stages in the training pipeline with different chunking and splitting strategies.

in order to realize a competitive SMT system using our bootstrapping technique, we would first need to understand the qualitative and quantitative effects of using different models and the respective chunking and splitting strategies.


The problem can become intractable even on the span of short sentences: a linguistically motivated context, such as a paragraph for instance, can not be handled.

such approaches can not be used for applications where real time is a necessary constraint (image retrieval, machine translation, augmented reality).


The search-space corresponds to all the possible sense combinations for the text being processed.

a configuration C of the problem can be represented as an array of integers such that j = C[i] is the selected sense j of w i .


Depending on the lexical information present and the structure of the graph, ants will favour following bridges between more closely related senses.

the more closely related the senses of the nests are, the more a bridge between them will contribute to their mutual reinforcement and to the sharing of resources between them (thus forming meta-nests); while the bridges between more distant senses will tend to fade away.


Furthermore, at each cycle, there is a slight (linear) evaporation of pheromones (penalizing little frequented paths).

ϕ t+1 (A) = ϕ t (A)×(1−δ), where δ is the pheromone evaporation rate.


Indeed, the closer a node is to a given nest, the more ants from that nest will have passed through and deposited odour components.

the odour of that node will reflect its nest neighbourhood and allow ants to find their way by computing the score between their odour (that of their mother nest) and the surrounding nodes and by choosing to go on the node yielding the highest score.


If one annotation (say α s ) is more learnable than the other (with respect to p, l) in a relative portion r of these pairs, we say that p is r-biased towards α s with respect to l. If for some s ∈ S, it holds that for every p ∈ P, l ∈ L, p is r-biased (r ≫ 0.5) with respect to l towards the same annotation (say, α s ), we say there is a unanimous r-bias towards α s .

α s is the empirically preferred annotation of s. Varying syntactic structures are prevalent in many syntactic formalisms (see Section 7).


Finally, no considerable bias is found in the infinitive verb structures, as a 0.7-bias towards any alternative is found in only one setting.

our experiments suggest no preference towards either alternative in this case.


Our current goal is to get the most out of supervised training data by leveraging unannotated data via selectors (no use of a knowledge-base or similarity metrics).

our system achieves state-of-the-art results in line with top supervised systems while our earlier knowledge-based approaches produce results in line with systems not utilizing training data.


In this work, we focused on the linguistic content.

we deal with meta-language tokens only when they are embedded within or adjacent to purely linguistic content (e.g., the tweet itself, provided it consists of one or several sentences).


Note that many contractions and typographic diaeresis are built around a verb which is prefixed by a clitic.

such contractions can project function labels and most of the time are the head of the sentence.


The final form of the word determines its syntactic role within a sentence.


However, in reality, in an ensemble system all the classifiers are not equally efficient in detecting all types of output classes.

while combining the classifiers using weighted voting, weights of voting should vary among the different output classes in each classifier.


Henceforth, all the Bengali glosses are written in ITRANS notation 2 .

final class label selected for this particular token is 'LOC' as f('LOC')>f('PER').


Our first baseline which is constructed by including all the features in CRF model yields the recall, precision and F-measure values of 87.75%, 85.27% and 86.49%, respectively.

the DE based feature selection technique improves the F-measure value by 0.82 points.


This may be due to the fact that all the datasets are highly imbalanced, and hence greatly biased towards the negative categories.

there are not enough NE instances that could be more effective for NE identification.


Two heads of the same lexical category are not allowed inside a chunk.

'Ram's son' in English and 'rām kā betā' in Hindi will have two chunks each [Ram's]  The task of the chunker is to divide a sentence into chunks leaving out some words that are not grouped into any of the identified chunks.


The tagger may tag 've'as DEM 'demonstrative' but, as a demonstrative, it does not concord with the head noun for the relevant case feature.

the tag DEM is rejected and PRON is selected.


The expression given in 30 below is used to detect VGs in a given sentence.

start Marker (Intermediate marker)* Must-end marker the start-marker and the must-end markers are obligatory to form a VG while intermediate markers are optional and may recurse (*).


Our system, however, automatically learns from training data what constitutes an easy decision.

we can utilize more features, learn more precise weights, and adapt to any dataset for which training data is available.


Inspired by Goldberg and Elhadad's (2010) approach to easy-first dependency parsing, we utilize a learning method that performs supervised perceptron-style updates as it carries out clustering.

during training, the learner observes partially completed clusterings similar to those that are likely to be encountered during testing.


actions is O(C n), where C is a constant.

3 One might think that computing the score of each new JOIN i j action would be expensive (because one might have the total runtime is O(n 2 log n) + O(sn log n), where s ≤ n is the number of actions popped before HALT and tends to be small.


However, what we are evaluating is a JOIN i j action that merges a pair of clusters.

when we consider joining two clusters i and j at least one of which contains more than one mention, we have a choice of all local vectors between mentions in i and j to score the local compatibility.


At test time, maintaining the local feature vectors takes time O(1) since both the local features and the weight vector w are fixed.

we can maintain a single local feature vector for each action.


We believe that this is due to the large variation of the values of our actions.

we settled on a variant of the structured perceptron with early updates (Collins and Roark, 2004) with beam width 1.


The difference is motivated by the fact that our data is highly nonseparable-it is extremely rare to get all of the coreference decisions right in a document.

the algorithm needs to operate in states in which some errors were previously made.


Additionally, there is a significant overhead in initializing the necessary data structures for each document.

a straightforward implementation of the structured perceptron with early updates is inefficient.


Our easy-first approach, in contrast, does not attempt to join clusters that contain conflicting person names.

it correctly separates {Jack Welch's attorney, Daniel K. Webb and who} into one cluster and all other mentions into another cluster.


For instance, if j is headed by a pronoun and cluster i contains the only reasonable antecedent for J, the algorithm can have more confidence that i and j should be joined.

it would learn to perform such actions sooner.


On average, across both English and Chinese data, in 44.8% of dialogues different participants held the roles of the (task) Leader and the Influencer.

a significant portion of the MPC corpus has distinct participants exhibiting sociolinguistic behaviors consistent with either Leadership or Influence but not both.


Language evolution is reflected in documents available on the web or in document archives but is not sufficiently considered by current applications.

not knowing about different names referencing the same entity severely compromises system effectiveness.


However, global measures can help to find direct co-references such as Barack Obama and Barack Hussein Obama.

to fully capture temporal co-references we need, in addition to global relatedness measures, a relatedness measure that captures how related terms are during the time periods where they can be related at all.


A decrease in the threshold would lead to a lower precision.

a low threshold is sufficient to get an upper bound of the recall while maintaining precision.


Experiments when developing METIS-II have shown that the solution of the assignment problem is computationally-intensive.

to conform to the strict translation time constraints set for PRESEMT, the Gale-Shapley algorithm is used (Gale andShapley, 1962 andMairson, 1992), which solves the assignment problem in a reduced time.


However, the range for PRESEMT-1 is displaced towards lower values of BLEU in comparison to PRESEMT-2, while also a larger number of outliers exist for PRESEMT-1.

most median values of PRESEMT-1 for different sentence sizes are placed at lower BLEU levels, below the 0.15 mark, with only a few outliers exceeding the limited range of the boxplots.


The usability of the corpus is further compromised by idiosyncrasies in the data such as topic bias (as shown by Brooke and Hirst, 2011) and the occurrence of characters which only appear in essays written by speakers of certain languages.

it is hard to draw conclusions about which features actually perform best.


Because techniques for NLI have looked for patterns in the data not only at the lexical level but also at the character level, any unintended lexical or character patterns correlated with L1 are likely to be weighted heavily by a statistical classifier.

it is important that the corpus is free of such patterns.


It has been a challenge, however, to evaluate whether documents or even individual terms are actually anomalous outliers within the specified area and timespan or just perennially prominent terms like stopwords or certain keywords that are frequently used in the examined region or throughout the whole Twitter network.

relevant and anomalous documents and keywords are often obscured by falsely highlighted keywords resulting from regular day-to-day chatter.


Besides globally prominent terms, which can easily be detected by tf-idf, there are also many terms that are only prominent within a certain region.

when examining a certain geospatial area and timeframe of documents, the importance of certain prominent and unusual terms can still be easily obscured by numerous and ordinary terms for the area.


However, all these approaches examine the situation where an unknown document is given and the most probable location of its origin has to be found.

it is reasonable to estimate the probability that a certain term or document appears at a certain location.


In this case the retrieval from memory has to be fast enough to avoid interrupting fluid interactivity for the user.

it depends on the application what this means in terms of retrieval speed, i.e.


Most of these approaches, especially the bootstrapping-style methods (He and Gildea, 2006b;Lee et al., 2007;Kaljahi and Samad, 2010;Fürstenau and Lapata, 2009), have achieved minimal or even no improvement from using unlabeled data.

the development of effective semi-supervised techniques remains an important and largely unresolved problem.


Though the BayesSRL model exploits a cross-predicate representation, it does not align roles across predicates which prevents us from using supervised evaluation metrics.

we evaluate the methods using clustering measures: the harmonic mean of purity and collocation, a common metric for unsupervised SRL evaluation (Lang and Lapata, 2010), and the information-theoretic V-Measure (Rosenberg and Hirschberg, 2007).


door in sentence (b) will have the same key as Mary in sentence (a).

this introduces an upper bound on the model performance: in our experimental set-up the upper bound on the purity of clustering was equal to 91.7%.


In sum, BayesSRL treats the unsupervised semantic role labeling task as clustering of argument keys.

argument occurrences in the corpus whose keys are clustered together are assigned to the same semantic role.


The set of permissible alternations is predicate-specific, 3 but still most of the alternation are shared across several or many predicates (e.g., passivization or dativization).

bayesSRL regards semantic role induction as a multi-task clustering problem and encodes the relative 'popularity' of alternations by quantifying how likely a pair of keys is to be clustered.


In fact, as we will see in Section 4, labeled data mostly provides evidence for combining clusters (thus increasing collocation), and, consequently, the ability to learn granularity is crucial.

a compromise is necessary between (a) learning the granularity from labeled data and (b) limiting the influence of unlabeled data on cluster granularity.


Although this first experiment proved that RTE systems are not effective in the task, a manual annotation of the experimental data set showed that part of the Restatement examples are also entailing, suggesting that the relation of textual entailment may be a subset of Restatement relation.

rTE techniques could be explored to solve the task on this subset of sentences.


In order to avoid noise in the data, we extracted only those instances where the IC verb was the main verb of an Arg1 which contained only a single sentence.

we simultaneously queried the PDTB annotation and the syntactic annotation of the Penn Treebank.


In order to learn P r nbest , we add the MT system as an intermediate component, which creates a translation model from the token alignments, and then applies it (along with a language model) to the query text, using a decoder.

the distribution is informed by the query context and its derivation.


In practice, we would like to select model parameters without observing all the test topics.

we ran 10-fold cross-validation experiments on each collection, selecting parameters that maximize MAP on nine folds and evaluating on the remaining one.


With more complex representations, it is possible to increase effectiveness, but at the cost of efficiency.

a desirable CLIR approach would express all the relevant information and nothing more.


Most of these new words will now be low-frequency words -again we observe the very same Zipfian phenomenon, and the problem of low-frequency words is still present.

we have decided to take an opposite path, where "removing" data from the initial corpus (that actually means sampling a sub-corpus with less data items from the original large corpus) and properties of low-frequency words (Moore, 2004b;Prochasson and Fung, 2011) should actually help us detect correct cross-lingual word associations.


Additionally, we should assign more weight to translation pairs that fulfil the strict criteria in sub-corpora of larger size K. For instance, if we detect that two words have identical frequency distributions and have fulfilled all the criteria from Subsection 2.2 in a sub-corpus consisting of a few millions items, that evidence should be more important than detecting that the two words could be extracted from a sub-corpus comprising only a few sentences.

for each potential translation pair t i j we assign a corresponding overall score c t i j .


However, having more iterations implies obtaining more different evidences from different sub-corpora and assigning more significance for the extracted candidates (see Subsection 2.3.3).

we have decided to use 1000 iterations for safety.


However, our claim is that translation pairs obtained by SampLEX are of higher quality than those obtained by the baseline systems.

with this experiment we want to answer the following question: "Are translation pairs obtained by the SampLEX algorithm really more accurate than translation pairs obtained by other methods?".


While both the ILIAD and CNET datasets are mainly troubleshooting-oriented and technical, they come from different domains.

we expect the DA and Link distributions of them to be different.


(2007) experimented with various learners from three machine learning software packages, namely LIBSVM (Chang and Lin, 2011), TiMBL (Daelemans et al., 2010) and Weka (Hall et al., 2009), and found that LIBSVM performs superiorly on the Solvedness classification task.

lIBSVM is used for Solvedness classification in this research.


In our initial experiments, we experimented with different kernel functions for LIBSVM, including linear, polynomial, radial basis function (RBF) and sigmoid kernels, and found the linear kernel to outperform other kernels.

lIBSVM with linear kernel is used throughout our experiments.


In most cases, the evaluation target and the evaluative expression are near to each other.

we add the distance label between w i and the evaluative expression as a new feature for evaluation target extraction.


We rely on the analysis and guessing tools in the second aspect and, in particular, on their ability to reduce the search space for the disambiguator.

we have chosen to implement a second-order variant of CCRF which incorporates a dense feature set (constraints from the training set are taken into account during the construction of the feature set) with no sparse forward-backward recursions optimization.


We haven't recorded any improvements in the tagging quality when using prefix, suffix or shape features as observations for known words.


If an edit is under the scope of negation, a quantifier or a conditional, then r e i is mapped to an appropriate semantic relation r P e i based on that context.

r P e i can be seen as the sentence-level semantic relation between T and the sentence which can be obtained by applying the edit e i to T .


We set the derived semantic relations to independence (#) for the rules which derive more than one semantic relations.

as with Ψ P , given two semantic relations r C i−1 and r P e , the joined relation of the variable r C i is uniquely determined.


The weight of each factor is represented in connection weight matrix U.

it can address the optimization problem well in factored n-gram LM (Duh and Kirchhoff, 2004).


The advantage of this approach is that it does not need any original target language data for training.

it can be widely used for any new target language as long as a source-target-language machine translation engine is available.


Nevertheless, the parallel texts in multiple languages can be approximately taken as label-conditionally independent multiple views of the same set of data objects, and their simple feature space expanding method is still far from fully exploiting this multilingual information.

in this work we investigate new multi-view AdaBoost approaches to improve the performance of multilingual subjectivity analysis.


Following (Banea et al., 2010)'s suggestions, we assume that only the words in italics carry potential subjective meaning and their surrounding contexts would be objective if without them.

their association with an either subjective or objective sense imparts the same label to the whole segment.


To generate subjectivity labeling for target languages, they projected the original sentence-level subjectivity annotation from the source English data onto the target language data.

we get a multilingual subjectivity analysis dataset with six languages.


Moreover, though we perform representation learning in a semi-supervised manner, we only exploit the existing labeled data in the source domain.

our model can be applied to arbitrary new domains without any extra annotation effort.


Likewise, a sentence containing "path-way" is almost certainly from a biomedical literature rather than from a newswire article.

with traditional representations of NLP, a prediction model trained in one source domain can hardly work well in a different target domain.


Given the model parameters θ , the hidden state values of sequence Y are computed using the Viterbi inference algorithm used in HMMs.

the trained logistic regression model only requires a model parameter matrix W in the size of L × (2L + V + S + 1) to calculate the probability P(Z t |Z t−1 , Z t+1 , X t , Y t , W ) for any inputs.


Note in the DDN model we introduced, dropping the label layer Z does not affect either the structure nor the parameter of the other two layers, but simplify a DDN model into a HMM.

we can use DDNs as HMMs on unlabeled sentences by sharing common model parameters across labeled and unlabeled sentences.


Due to the independency assumptions in such generative models, it is hard to extend them to incorporate arbitrary features, especially word alignment information as used in the traditional two-step pipeline.

despite theoretical advantages of these models over the two-step pipeline, in practice they can only produce comparable translation quality with the two-step pipeline in some scenarios, and usually even worse results.


A cube represents the space of hyperedges for a source parse, where each dimension of a cube denotes the candidate list of a substructure for a hyperedge.

the point in a cube corresponds to a hyperedge (see Fig.


Figure 2(b), for point (0,0), since the word "zhiyi 3 " aligns to "one 1 " and "of 2 ", and the tail node is X 0,2,2,5 , the max target coverage is (0, 5).


When we incorporate the sparse features learnt by our training algorithm, we achieve a further improvement of +0.43 BLEU point.

our training algorithm is able to learn the useful information encoded by the sparse features for translation.


Furthermore, the grammar in UDSGI is smaller than the grammar of the baseline (76.7%).

we believe that our improvement comes from the theoretical advantage of our approach, and that such advantage does exist even on large-scale data.


For example, in the NUS Corpus of Learner English (NUCLE) corpus (Dahlmeier & Ng, 2011b), there are a total of 3,302 preposition mistakes out of a total of 147,087 prepositions.

to build confusion sets for open class words such as verbs, one would need a very large annotated corpus.


We consider several synsets including: "event", "human_action", "human_activity", "act", "phenomenon", and "action" to be eventive.

when hypernyms of a sense fall into these synsets, we will consider this sense to be eventive.


According to TABLE 2, 2,488 out of 4,237 events (i.e., 58.7% of events) have class attribute with value of "occurrence".

a baseline system that assign "occurrence" to all events, will achieve 58.7% accuracy.


Whereas, those features were produced by paying no attention to avoiding similar sentences to get close scores.

in order to keep low information redundancy in the finally generated summary, those authors had to apply diversity penalty algorithm to update the sentence orders which resulted from the training function directly.


To account for the unseen words for s, we smooth the above equation like this: λ is a trade-off parameter and is set to 0.7 in experiments.

we can talk about the generation probability of sentence u given another sentence v as: We use the length of u (i.e., |u|) to conduct normalization so as to avoid the tendency that longer sentences get smaller generation probabilities.


Ideally, an LM should be trained with a large-scale matched corpus, but in many cases this is not realistic.


If we build an experimental dataset in this ratio, i.e., 8,525 vs. 283,000, the number of correct sentences will dominate the system's performance.

we build a balanced dataset to prevent this bias and to have a fair examination.


This is a with-segmentation approach C˙S.

when we compute frequencies of word pairs, there are six combinations C gS , C gW , C bS , C bW , C p2S , and C p2W , where the subscript g means the Google Chinese Web 5-gram corpus (GC5g) is adopted, the subscript b means the Chinese Web POS 5-gram corpus (CP5g) is adopted, and the subscript p2 means Equation 4and CP5g are adopted.


Their performance, 65.01% vs. 65.09%, does not have a significant difference in NAT-HSK datasets.

we conclude that the two different segmentation systems do not have different effects.


This phenomenon also holds in HSK-HSK datasets.

we conclude that using segmentation systems result in better performance.


This requirement always leads to a severe sparsity problem for translation.

the global reordering problem is not well addressed in these models.


They not only achieved the start-of-the-art monolingual SRL performance to date, but acquired the mapping between bilingual arguments.

we follow their work to achieve bilingual SRL results for our training set.


If we ignore it and its target counterpart, the remaining PAS is still reasonable.

we extend the PAS transformation rules based on this insight.


We believe that they complement each other because they perform translation from different angles.

to capture this complementation, for the PAS span in the decoding hypergraph, we can use both our PAS-based translation method and the traditional translation method.


Among the rules, 99.55% of their TPs can be binarized.

our decoding algorithm in sub-section 3.3 can be used in almost all cases.


Due to the narrow decoding space and bad PASs, the comprehensive translation score of PASs' translation candidates would be too low to be utilized in system PAS(BTG).

numerous PASs are bypassed by the decoder and only a slight improvement is achieved by system PAS(BTG).


Each of these PASs belongs to one individual sentence because they all cover more than 50% words of the sentences.

88.5% (900/1,017) sentences in PAS+BTG system and 75% (290/386) sentences in PAS(BTG) system have utilized these senwide PASs, by which the skeleton structure of sentences are well modeled for translation.


2) Numerous parse trees are not compatible with word alignment which is generally learned by GIZA++.

a number of useful translation rules are often excluded.


As a whole, compared with previous works, our unsupervised trees are generated fully depending on word alignment.

by using our tree structures, the incompatibility problem between tree structures and word alignment can be well resolved.


Such many parameters would cause a difficult estimation problem, especially for the EM algorithm adopted in this paper.

to reduce the huge generation space of tree structures, we first segment the bilingual sentence pair into several shorter ones and then impose frontier node assumption to construct the packed forest.


Obviously, Equation (1) guarantees that the sub-tree rooted at span [, carries the largest number of fronter nodes.

in a bottom-up manner, when we arrive at the node covering the whole sentence, we can achieve all the tree structures with the larget number of frontier nodes.


The unsupervised tree structures are constructed depending on the word alignment.

they are naturally compatible with word alignment and lead to a better rule coverage.


To achieve this we need to have an abbreviation dictionary, which is laborious to manually maintain because the number of abbreviations increases rapidly (Chang and Schutze, 2006).

it is helpful to automatically generate abbreviation from full forms.


By re-ranking using web data, additional information is incorporated.


They treat POS tagging as a submodule of constituent parsing.

we can also adopt a PCFG model for joint Chinese POS tagging and dependency parsing.


The stacked learning is implemented using a two-level architecture, where the level-0 consists of one or more predictors of which the results are exploited as input to enhance the level-1 predictor.

either JGraph or JTrans can be chosen as the level-1 model.


Correspondingly, assuming the output of JTrans is (t JTrans ), the score function of JGraph(JTrans) becomes: We can see that the feature functions are modified to include additional argumentst JTrans and d JTrans .

new features related with the additional arguments will be produced.


Correspondingly, assuming the output of JTrans is (t , the score function of JTrans(JGraph) becomes: The Guided Graph-based Joint Model: JGraph(JTrans) The Guided Transition-based Joint Model: JTrans(JGraph)  We can see that the feature functions are modified to include additional argumentst JGraph and d JGraph .


During the stacking, the model can learn that JConst is good at the dependencies with long dependency length.

the final stacking model can perform well for long dependency length, which is shown in Figure 5.


The available labeled data for entity linking is only for a certain number of names.

as shown in Figure 1, the previous approaches disambiguate a mention of the name (e.g.


Moreover, the positive-negative instance ratio of each name is also very different from others.

the entity linker generalized beyond labeled names ("Hoffman", "Chad Johnson", etc.)


However, since Bunescu and Pasca (2006) used Wikipedia hyperlinks to train the SVM kernel, Cucerzan (2007) used Wikipedia collection and news stories as the development data, and all of the three Wikification work (Mihalcea and Csomai, 2007;Milne and Witten, 2008;Ratinov et al., 2011) generalized their ranker on the data generated from Wikipedia without considering the knowledge of the queried name, they also fall into the traditional entity linking framework and suffer from the distribution problem in the previous entity linking systems.

we believe that our proposed lazy learning approach also can benefit these two tasks.


Because the knowledge base usually contains millions of entries, it is time-consuming to apply the disambiguation algorithm to the entire knowledge base.

the following pre-processing process is conducted to filter out irrelevant KB entries and select only a set of candidates that are potentially the correct match to the given query (a query consists of a name mention and its associated document text).


feature "acronym" is true), certain surface features effective for linking to "Communist Party of China" may be also effective for disambiguating "NY", and vice versa However, with the gap in other aspects between the distributions of A q and M shown in Section 1, directly adding M to our training set will produce a lot of noise with respect to the queried name.

instead of using all the distribution knowledge in M, we propose to only incorporate the shared knowledge with A q from M into u estimation based on structural learning.


However, in data set M, some instances related with some particular names may share more predictive information with the queried name than other instances.

in this work, we group the instances in M based on the "name", and then learn the shared information from the "name" group instead of individual instance.


Thus, one of the key factors for the success of our lazy learning model is whether we can obtain A q for the queries.

firstly we investigate the amount of the labeled instances created for each query.


For these queries, we can directly link them with NIL without disambiguation.

we only need to create A q for the remaining 1,891 queries.


In contrast, previous approaches usually trained their model on thousands of labeled instances.

it suggests that we need more labeled instances for some queries and it is necessary to still leverage the manually labeled data set M in our learning process.


Because our lazy learning model generalizes on both the distribution knowledge learned from A q and the predictive structure Θ shared by A q and M, the effectiveness of such shared structure Θ is another key factor for the success of our lazy learning model.

inspired by the work (Ando, 2006) for WSD, we design a set of experiments to investigate the configuration of Θ.


Intuitively, the predictive structures of the names with the same NE type may be more similar than those of cross-NEtype names.

except for the feature split discussed above, we explore another two Θ configurations.


The other learns Θ from A q and the subset of M, where the instances have the same NE type with the query.

we experimented on our development data set with the combinations of the two types of Θ configuration, i.e.


We are also interested in whether all the feature groups are suitable for learning Θ j .

we further experimented with F={SeF, SuF, GS}, F={CF, SuF, GS}, F={CF, SeF, GS} and F={CF, SeF, SuF}.


In addition, E = D −1 M , where D −1 is the orthogonal matrix which is used to transform the matrix M to E. Moreover, when matrix E and B are written as E = D − 1 2 BD 1 2 and B = D − 1 2 M D − 1 2 , we can see that E is similar in structure with matrix B.


Though noisy, they still reflect partial facts of the "true" weights and thus need to be preserved to some extent.


Furthermore, as we discussed in Section 2, given the multiplicative property of lognormal distributions, for each inflectional class, the generated word distribution is also lognormal.

with the proposed model, we can directly capture the statistical property of word frequencies without the need of an additional generative process.


For the multinomial model, we sample (c, t, f ) together, with the following posterior conditional probability, P ((c, t, f ) Take the second term of the above equation as an example.

of our choice of conjugate priors, the posterior distribution, P(t|c, θ T , A −i ), is also multinomial but with a different parameter α ′ ∼ Dirichlet(α F + m c, f ), where m c, f is the number of analyses that contain both inflectional class c and suffix f .


Our observation of the user behaviors reveals that queries close to each other in a session are more likely to be paraphrases than those far apart.

given a pair of related queries 〈q i , q j 〉 from a session QS, we measure the distance between two queries from three aspects: • Query based distance dq.


We find after analyzing the data that, query pairs like "_dÈ'Ë p (Justin Bieber height)" and "_dÈ'Ë p N-(Justin Bieber height and weight)" are quite common in users' search sessions, in which a query is expanded by adding a word closely related to the original query words.

many closely related non-paraphrase word pairs, like p (height) and N -(weight), get large a − scores and thereby penalized by the lexical score features.


To make use of the large-scale archives of question-answer pairs, it is critical to have functionality of helping users to retrieve previous answers (Duan et al., 2008).

it is a meaningful task to retrieve the semantically equivalent or relevant questions to the queried questions.


First, an original language word can be translated into multiple candidate words in a foreign language.

the word ambiguity problem can be resolved during the translation in a given context of an original language word.


Conversely, various different original language words that refer to similar meanings are translated into a single word or a few words in a foreign language.

the lexical gap problem in the original language is to some extent ameliorated by using the translated words in a foreign language.


Let R E (q, d) be the rank of historical question d for a given queried question q, and let R F (π(q), π(d)) be the rank of translated representation π(d) for a given translated queried question π(q).

we define a refined score Scor e(q, d) based on the following function: where By applying the refined ranking strategy, we obtain the refined ranking model shown in Algorithm 1.


Moreover, DicTran performs translation without taking into account the surrounding words as contextual information, while GoogleTrans and YahooTrans are context-dependent and thus produce different translated Chinese words depending on the context of an English word.

the word ambiguity problem can be resolved during the English-Chinese translation in a given context of an English word.


Figure 2(Left) shows that, for MAP, E + C performs better than baselines E and C when α ∈ (0.2, 0.9).

a relative broad set of good parameter value is observed.


Whereas while taking an appointment, it is plausible the need to annotate the name of the doctor, in a social call with a friend, the name of the friend is part of the background knowledge of the user.

while from a content (and an NLP) point of view both names are Person NEs and carry the same amount of information, from the point of view of the user they might have different weight (no need of taking note vs need of taking note).


We are not aware of comparable hierarchical segmenters other than that in (Eisenstein, 2009) which, unfortunately, is no longer publicly available.

we compared the trees built by HAPS to the results of running iteratively two state-of-the-art flat segmenters.


BSeg was tested with and without that parameter.

rows 3 and 4 in Table 2 correspond to baselines considerably more informed than HAPS.


As in Ex(1), the sentiments toward the agent and theme, the sentiment toward the gfbf event (positive or negative), and the polarity of the gfbf event (gf or bf) are all interdependent.

rather than having to take a pipeline approach, we are able to develop an optimization framework which exploits these interdependencies to jointly resolve the ambiguities.


We are only able to evaluate true hits of gfbf events.

the input to the system is the set of the text spans marked as gfbf events in the corpus.


We may have two candidate agents for a gfbf and that we will choose between them.

only one agent is assigned a positive or negative label; the other is considered to be an incorrect agent of the gfbf (similarly for the theme candidates).


In the corpus of DCW, only the writer's sentiments toward the agents and the themes of gfbf events are annotated.

since there are many false negatives of sentiments toward entities, the corpus does not support training a classifier.


Thus, since there are many false negatives of sentiments toward entities, the corpus does not support training a classifier.

we adopt the same local sentiment detector from , using available resources to detect writer's sentiments toward all agent and theme candidates.


Thus, if there is more than one gfbf in a sentence, and the path between the two gfbfs in dependency parse contains only conj or xcomp, and there is no other noun between the latter gfbf and the conjunction, we assume the two agents are the same and the sentiments toward them should be the same.

for any i, j ∈ Entity, if i, j co-refer 7 , or they are the same agent as described above, Coref (i, j) = 1 (otherwise 0).


The framework chooses among the combined options.

for comparison, we build a baseline system that combines the outputs of the local agent/theme candidate detector and the local sentiment detector.


We set Iter = 100 empirically in rest of the paper.

the overall running time of GNMFNC is linear with respect to the size of word vocabulary, the number of questions and categories.


In the process of manually judging questions, the annotators are presented only the questions.

there are in total 20,000 judged question pairs.


In other words, we set α p = a Ks×Kp and β l = a Kp×K l .

we tune the parameters α p and β l by alternatively adjusting the common factor a via grid search.


Consider the regularization term used in equation (2), each element in U T s U p and U T p U l has a value between 0 and 1 as each column of U s , U p and U l is normalized.

it is appropriate to normalize the term having ∥U T s U p ∥ 2 F by K s K p since there are K s × K p elements in U T s U p .


The common factor a is used to adjust a trade-off between the matrix factorization errors and the mutual orthogonality, which cannot directly tune on the validation set.

we look at the objective function of optimization problem in equation 3on the training data and find the optimum value for a.


We can see that the optimum value of a is 100.

the common factor a can be fixed at 100 for our data set used in the paper, resulting in α p = β l = 0.625.


In our experiments, we have associated equal probability to each of these mutation operations.

each mutation is applied in 33% cases of the cases.


We can restrict the effective size of X (2) by assigning zero probability to the vast majority of its elements, by constraining the hypothesis space to consider only cluster assignment functions q(x i |c i ) in which n 2 << V 2 of the elements have nonzero probability.

every word sequence in V d can be generated by the d = n segmentation, but every other segmentation is constrained to generate at most a subset of V d with nonzero probability.


The Renyi α-entropy of a random variable with some probability distribution q is defined to be H α (q) = α 1−α log q α and there are two useful results we use (Principe, 2010): 1) lim α→1 H α (q) = H(q), where H(q) is the Shannon entropy; and 2) H α (q) is non-increasing in α.

for α < 1, H α (q) is an upper bound on the Shannon entropy.


Thus, for α < 1, H α (q) is an upper bound on the Shannon entropy.

since l α regularization minimizes the Renyi α-entropy, which for 0 < α < 1 is an upper bound on the shannon entropy, it effectively maximizes the mutual information between c i and c i−1 , given that we have shown that at least in the context of clustering, sparsifying both the observation probabilities and the state transition probabilities of an HMM using the l α prior directly minimizes generalization error.


Particularly, we introduce the morphological knowledge as both additional input representation and auxiliary supervision to the neural network framework.

beyond word representations, the proposed neural network model will produce morpheme representations, which can be further employed to infer the representations of rare or unknown words based on their morphological structure.


Moreover, we introduce extra coefficients into the network to balance the weights between word embeddings and morpheme embeddings.

in the back propagation stage, we will update the word embeddings, the morpheme embeddings, and the balancing coefficients simultaneously.


By using negative sampling, the training time spent on summing up the whole vocabulary in Formula (2) is greatly reduced so that it becomes linear with the number of the negative samples.

we can calculate the gradient of G(v I ) as below, In the back-propagation process, the weights in the matrices M and M are updated.


As introduced in Section 4.2, in the fixed weight model the ratio of weight of morphemes to the weight of word is 0.25; while our experiment records show that the averaged ratio are 0.43 if the two weights are updated, meaning that the weight of the combination of morphemes increases and the contribution of the original word to the final combined embedding decreased.

the syntactic accuracy which largely reflected in the morphological structure of a word increased, but the semantic accuracy hurts a little.


However their embedding vectors are obtained through a tf-idf counting model, which is usually called as distributional representations (Turian et al., 2010), rather than through a neural network.

we do not regard their paper as very related to our work.


syntactic contexts into the continuous representation of phrases 1 .

the nearest neighbors in the embedding space of SSPE are favored to have similar semantic usage as well as the same sentiment polarity.


In some cases, the tweets are written with ungrammatical sentences with a lot of emoticons, abbreviations, specific terminology, slang, etc.

the usual techniques of NLP must be adapted to these characteristics of the language, and new approaches must be proposed in order to successfully address this problem.


One interesting aspect is that these kernels correspond to a scalar product in the fragment space, where each substructure is a feature.

they can be used in the training and testing algorithms of kernel machines (see Section 2.2).


In other words, a fragment is any possible tree path from whose nodes other tree paths can depart.

it can generate a very rich feature space.


Although we tried several parameters on the validation set, we observed that the default values yielded the highest results.

we used the default c (trade-off) and tree kernel parameters and a linear kernel for the feature vectors.


This high result can be explained considering that the size of the complete hypothesis set is smaller than 100 for most questions.

we can conclude that the N -best lists do include many good options and do offer quite a large space for potential improvement.


Sarcasm generally reverses the polarity of an utterance from positive or negative into its opposite, which deteriorates the results of a given NLP task.

correct identification of sarcasm can improve the performance.


Imbalanced distribution However, data in the real world do not necessarily resemble the balanced distribution.

we have also performed the evaluation on an imbalanced distribution.


First Available (FA) principle: the closest dual polarized valencies with the same name are paired.

the elimination of the dual pairs ( C) ( C) and ( C) ( C) defines respectively left and right non-projective dependencies labelled by C.  We conduct a three-step transition-based parsing.


Moreover, in order to evaluate the adaptation of our method to standard treebanks we would like to perform the method on data for which the anchors would have been artificially created.

we build a second treebank from the first one, which we call the CDG Treebank 2, in which the original anchors are replaced by artificial anchors generated by the projectivization step of the pseudo-projective method of Nivre and Nilsson (2005).


The grammar and the treebank were developed simultaneously.

a large part of the sentences were used to develop the grammar and were chosen to cover as much as possible the syntactic phenomenon of French.


Indeed, the projective step allows to find the anchors which are a kind of projective dependencies, so there are easier to predict than the non-projective dependencies.

the label accuracy of the non-projective dependencies takes advantage of the good results of the anchors which were not paired with a non-projective dependency during the second and third parsing steps.


In fact, finding the right non-projective dependencies by performing the non-projective step from left to right seems almost infeasible because it is essential to find the dependent first.

the problem comes essentially from the bad prediction of the anchors during the projective step.


The method needs a learning corpus that associate to each non-projective dependency a projective anchor.

the method is well adapted to CDG treebanks.


However, different from multi-view clustering problem, our goal is to recover a sparse rating matrix with a large number of missing user-item pairs rather than merely exploiting cluster structure with full task information.

the straightforward multi-view representation of the objective (rating matrix) is not available.


Instead, we use side information (user social graphs) to exploit multi-view learning for improving collaborative filtering (CF).

collaborative topic regression (CTR) (Wang and Blei, 2011) is employed as our basic learning framework with side information.


In reality, a large amount of diverse social graphs data are widely existed and particularly valuable for mutually reinforcing each other.


In contrast with the first strategy, we model the latter using a shared latent social structure for all views but with different user representations.

we can relax strong consensus assumption in our heterogeneous case, through linear combination of each sub-latent user with maintained sharing mechanism.


To achieve this more weaker sharing mechanism, we are towards its transformed perspective, i.e., sacrificing heterogeneous characteristic, because we need to exploit shared information from latent graph specific feature perspective.

we require equal dimensions of different graphs, which motivates us to investigate the homogeneous case.


For other similar structures, i.e., V T D c i V , S T D k q i S, and so on, they are similar.

we can significantly speed up computation by this sparsity property.


This is natural but the opposite is hard.

our solution 'Our-Homo' for modeling weak consensus is effective for both homogeneous and heterogeneous cases.


More complex approaches usually require resource-specific feature engineering, which makes their transferability to other resources or languages difficult.

we present a framework which combines different similarity measures in a generic and flexible way and enables state-of-the-art WSA performance on a variety of resources with modest effort.


However, for none of these features we could observe any significant 1 impact on the results, mostly due to sparsity of the respective features.

we do not report them, but on the other hand we consider this an indicator that gloss similarity and distance in the resource graph already sufficiently capture the similarity between senses.


Another factor are the gold standards, which are quite different in terms of size and composition (see Table 1).

no classifier is the undisputed "winner", but Bayesian Networks proved most robust in our experiments, showing competitive results in every case.


Both Zhu (1982) and Chen (1999) state that if two phrases have constituents with the same phrasal categories and the dependency types between them are also the same, the phrasal categories of their combinations must be the same.

it is natural to derive the category of a phrase from the phrasal categories of the immediate constituents and the dependency type between the constituents.


When converted into DS, both lead to the correct link, with "i ² (dawn)" being the SBV modifier of "ü (come)" (Figure 3(c)).

the PS parser error is erased in the conversion into DS.


our task is to annotate clause pairs in a document with one of the discourse relations given above.


This feature type captures the POS sequence of c. A patent usually focuses on a narrow technological subdomain.

many of its terms are semantically related to each other.


In this case we define "long enough n-grams" as those compose by at least 3 capitalized tokens.

a web page W is initially represented as the sequence of tokens starting in uppercase, in the order as they appear in the web page.


4 For this reason, the total ordering given through the ratings may be difficult to predict.

we will conduct experiments both on ranking and on classification where the rating range is binned into two rating classes.


We analysed the layout structure mode of each web site and built templates to extract topic title, publishing date, author, topic content and some other topic related informations.

a large scale Tibetan text corpus is built, which includes about 190 thousands documents, 67.21 million words, 93.66 million syllables and 265 million super characters in total.


Then, in the second step, some special case-auxiliary words ( which are all monosyllable words ) are used as separators to break the sentence into blocks.

both the forward maximum matching method and backward maximum matching method are used to segment each block into words.


To the best of our knowledge, no automatically accessible data annotated according to the reading difficulty level is available for Bangla.

we have developed a digital resource pool of Bangla text documents in Unicode encoding that can be used for various NLP tasks such as feature extraction, document analysis etc.


As mentioned, there is no annotated data present in Bangla, which can provide a direct classification of text difficulty for Bangla readers.

we have undertaken an effort to annotate the experiment texts with the target readers of Bangla.


Inter-rater reliability was measured through Krippendorff"s alpha 3 and = 0.81 was found.

we concluded that annotators agree more often than would have occurred by chance.


We have measured the correlation between the outcomes of two questions corresponding to each of the fifty annotators; and found that in each case the correlation was greater than 0.8 (p < 0.05).

the questions can be considered as equivalent, and subsequently we have considered the rating for the first question as user input for our readability models.


The third combination consists of all the features.

we have evaluated six different types of SVM and SVR models for each group.


In any case, the built network is not free of errors which are (or should be) corrected along their discovery.

a large number of obvious relations may be missing in the lexical network but are indeed necessary for a high quality resources usable in various NLP applications, or even crucial notably for textual semantic analysis.


Remnick notes that children's book author Kornei Churnosky praised her translations of Turgenev and Chekhov but was less than pleased with her rendering of Dosteyevsky, complaining that she had smoothed over the erratic and challenging original text of that particular author.

this work focuses on these claims of distinguishability in particular, for it is exactly these characteristics that can, in principle, be investigated using supervised learning techniques: Is it the case that one can automatically distinguish Garnett's renderings of Dosteyevsky from her translations of Turgenev, and if so, based on which textual characteristics, word distributions or individual word frequencies?


Nonetheless, in real applications, the TM database will deviate from the SMT training set when time goes by, because the TM database will be dynamically enlarged when more translations are generated by the human translator.

this paper will concentrate on a more realistic case, in which the TM database and the SMT training set are different and even from different domains.


However, the original integrated Model-III does not distinguish the newly added TM phrase-pairs from those original SMT phrasepairs in ( | ).

we introduce two new features Source Phrase Origin (SPO) and Target Phrase Origin (TPO), which are a member of {Original, Newly-Added}, to the original Model-III in  to favor the newly added TM phrase-pairs, and re-derive ( | ) as follows (assume that TPO is only dependent on SPO, NLN and ): The additional factor ( | ) in the above equation is added to handle those newly added TM phrase-pairs.


That is, the same source phrase in various places tends to have exactly the same translation in technical domains.

when we use Distinguishing Model to perform forced decoding, the obtained results would possess different statistics among the indomain development set and the out-domain training set.


However, the development set is not big enough (only a few hundreds sentence-pairs at each interval) to re-train all TM factors of the proposed model.

we simply add the following h 1 feature to reflect the tendency of having high translation consistency in the development set: Where ̅ and ̅ denote the source phrase, the target candidate, respectively.


Furthermore, various source synonyms might generate the same translation (Zhu et al., 2013).

even SCM≠Same, we still favor the SMT phrase-pair candidate which exactly matches TM target phrase.


On the other hand, although Model-III has greatly exceeded the SMT at each interval, Model-III + still significantly outperforms Model-III at most intervals.

the benefit of utilizing TM information and the benefit of adding TM phrase-pairs are not covered by each other and can be jointly enjoyed.


The reason is that the TM factors are trained on the news domain but the test set is from computer technical domain.

it is not strange that the Adaptation approach achieves the best translation results at all intervals in either BLEU or TER when the fuzzy match score is above 0.4.


However, they only refer to the TM information and do not regard the TM phrase-pairs as candidates during decoding.

the superiority of this approach disappears when the TM database and the SMT training set are different, because many TM phrase-pairs cannot be found in the original SMT phrase table in this case.


The main one is that they assume that both training and test data are independently and identically distributed.

when they are applied to data from a different distribution or domain they show poor performance.


Our work is similar to them except that OSM model is substantially different from the TSM model as it integrates both the translation and reordering mechanisms into a combined model.

both translation and reordering decisions can benefit from richer generalized representations.


incorrect name classing at the pre-processing step hurts the translation quality for the system name_t.


Transtac.oov has a high OOV and a high name ratios and all OOVs are names.

name classing improves the translation quality as long as the correctly classed names out-number the incorrectly classed ones, explaining the higher translation quality of name_t than the baseline.


They search for name translation candidates in large lists of English words/phrases.

they cannot accurately translate a name if the correct English name is missing in the word lists.


There is no 4-uple of d-factorizations, with d smaller than 3.


The reason for this is that analogical learning is quite computationally intensive.

tackling broader tasks, such as those typically considered in MT evaluation campaigns is currently too challenging.


Thus, word-based translation is not an adequate solution.

we devised engines which translate sequences of symbols (characters), without taking into account the notion of word.


Measuring how close a candidate translation is to a reference is of little interest here, since typically, a medical term only has one reference translation that we seek to discover.

rewarding partially correct translations (like a metric such as BLEU (Papineni et al., 2002) does) is not especially useful.


A training instance in their case was constituted by an input analogy, and the corresponding output equation along with one solution produced.

for the translation of the input form u, any pair ([x : y :: z : u], [x' : y' :: z' : c]), with x' , y' , and z' in τ (x' ), τ (y' ), and τ (z' ) respectively, and c a candidate translation would be considered for classification.


However, the dot product of these two vectors gives the cosine of the inner angle.

normalising by this gives a value of +/-1 according to whether accommodation movement is less or more than the amount required to meet the reader.


(2006), who found increasing dissimilarity with the words used by community forum users over a six-week period.

our results suggest that a large proportion of individuals within online communities have a tendency to differentiate themselves from others.


-Leonard Zelig, Zelig (Allen, 1983) We have presented a metric for measuring linguistic accommodation in a systematic manner, considering not only the context of an individual pair's communications, but the background models for both individuals.

the Zelig Quotient provides an objective, quantifiable measure of convergence and divergence in language use between individuals, as defined by the movement in an individual's typical linguistic style towards or away from the typical linguistic style of their conversational partner.


This is mainly caused by the phenomenon that any word profile P i,• becomes dependent of all other word profiles after an iteration.

we adopt a simpler method, gradient descent, in which P is updated by taking a small step in the direction of the negative gradient of (P P − I) 2 .


It is also worth noting that (P P − I) 2 requires each projection P •,k to be a unit vector because of P •,k P •,k = 1, which is infeasible when the nonzero values in X are large.

we multiply the matrix I by a coefficient c, which is calculated from the mean of the diagonal of P P in the current iteration.


It is important to point out that the data used by OrMFN, X , could be a very small subset of the whole dataset.

we only need to find neighbors for a small portion of the data.


In a large-scale data setting, it is impossible to manually identify relevant tweets.

we use Twitter hashtags to create groundtruth labels, which means that tweets marked by the same hashtag as the query tweet are considered relevant.


In order to use sense-specific embeddings as features, we should discriminate the word senses for the NER data first.

we further develop a novel monolingual word sense disambiguation (WSD) algorithm based on the RNNLM we have already trained previously.


The preliminary study mentioned in Section 3.1 provided evidence confirming that there are, in fact, differences in the behavior of particular lexical semantic classes with regard to their occurrence in unmarked contexts.

the next step consisted in determining the best way to make this information available to classifiers.


We also observed the amount of cues included in this set differed considerably from class to class (see Figure 1).

the larger the amount of marked contexts shared by the majority of the members of a class, the more homogeneous we can claim their behavior to be.


Although it is straightforward to recast skill inference as a standard text classification problem, i.e., predicting the skills with the profile text alone, personal profiles usually are poorly organized, even with critical information missing.

it is challenging to infer skills given the limited information from the profile texts.


For example, it is very likely that C++, C, and Python programming languages may co-occur in the one's profile, i.e., if a person has skill C++, it is highly possible that he would have the skills such as C or Python.

it is useful to integrate skill connection information when inferring personal skills.


In the literature, expert finding tends to consider each skill individually and seeks the most authority experts for each skill.

expert finding is always considered as a ranking process, i.e., ranking the experts from the candidates who are most suitable for the skill (Balog and Rijke, 2007).


The latter detects exactly those domain concepts that are annotated largely consistently in the training set of the ArguAna TripAdvisor corpus.


Before the discussion of these interesting asymmetries, we first present the method for direct projection of discourse relations using the GALE gold standard alignments, which we use to gather a set of explicit discourse connectives in Chinese.

far we have available a parallel Chinese/English corpus, discourse connectives automatically tagged with their senses on the English side and manual alignments of atomic units between English and Chi-  nese.


Based on our experience with the ACLD, it also corresponds to an acceptable frequency for receiving suggestions.

our test data comprises 26 two-minute segments, each of them ending at a speaker change.


For example, the graphic in Figure 1 ostensibly conveys the message that Toyota has the highest profit among the automobile companies listed.

infographics are a form of language since, according to Clark (Clark and Curran, 2007), language is any deliberate signal that is intended to convey a message.


Our vision is that since graphics have structure and content, the users whose particular information needs could be satisfied by an infographic will formulate their queries to indicate the requisite structure of the desired graphics.

we assume the use of full-sentence queries so that the semantics of the query can be analyzed to identify characteristics of relevant graphics.


For example, the topic concept for the country "United States" is likely to contain similar words to the concept for "China", such as the words "country", "nation", "region", "capital", "GDP", etc.

the probability of United States given China is likely to be higher than that of United States given the topic "rugby".


All of the models improve as k increases.

most important, both our mART and Random Forest models constructed from all 56 features perform much better than the baseline models for all values of k. we conclude that the use of structural and content features helps in selecting the most relevant graphic as well as the most relevant sets of graphics.


For example, the corpus is almost 30 times bigger than PDTB.

due to the large size of the corpus, it can be expected that eventual errors in the corpus (e.g.


Frequency Filter: This simple filter tries to account for the fact that low frequent events may affect the reliability of the LLR measure.

as a simple baseline filter, we have removed DC candidates that appear less than a certain number of times in the French corpus.


The second approach (called Syntax Tree Filter) makes use of Syntax Trees to filter unlikely syntactic combinations.

after parsing all the French: Le Livre blanc prétend résoudre ces problèmes , raison pour laquelle nous soutenons les English: The White Paper intends to resolve these problems and we therefore support these propositions qu'il contient.


In our experiment, we used a minimum threshold of 10 for this filter.

the filter removed additional 20% DCs, so that overall only 66% of LEXCONN's DCs are considered in the corpus.


NLP research has mainly focused on English and very few other languages.

there is a rich set of annotated corpora for linguistic analysis tasks for these languages.


In this case, the EM algorithm will be no longer needed, and therefore, the algorithm resolves all mentions in a single iteration.

this variant of our system can be considered as an efficient alternative to the Stanford system, that uses a simpler (pairwise instead of entity-based) and smaller (5 instead of 7 string matches) set of relations, and more importantly processes all mentions in a single iteration (instead of iterating over all mentions for each relation), and it still performs as well as its entity-based multi-sieve variant.


The more contents a user publishes on an area and the higher prestige a user has on social media sites, the higher likelihood that user can offer high quality answers.

the first goal is to precisely measure the relevance between question and a user's tweets.


In most cases, the head nouns are more important.


Only the brand names and product names are different.

using individual reviews for modelling is ineffective for finding product aspects or features, which are our topics.


For word representation, we use a matrix LT ∈ R n×|Vw| , where i-th column represents the embedding vector for term t i , n is the size of embedding vector and V w is the vocabulary of LT .

we can denote t i by a binary vector b i ∈ R |Vw| and get its embedding vector by x i = LT b i .


8), but OCDNN significantly outperforms DP-HITS.

the positive proportion score estimated by OCDNN is more effective than the importance score in DP-HITS.


For OCDNN, although error propagation is inevitable, the precision curve retains at a high level.

the joint approach produces more precise results.


The notion of distributional thesaurus, as it was initially defined by Grefenstette (1994), followed by Lin (1998a) and Curran and Moens (2002), is not often considered specifically, probably because of its strong link with the notion of semantic similarity.

the improvement of distributional thesauri has been first a side effect of the improvement of the distributional similarity measures used for their building and more precisely, of the distributional data they rely on.


On the other hand, Moby groups words sharing more extended syntagmatic and paradigmatic relations, including synonymy, hyper/hypo-nymy, meronymy, but also many more complex types such as the composition of co-hyponymy and hyponymy (abolition -annulment, cataclysmdebacle) or hypernymy and co-hyponymy (abyss -rift, algorithm -routine).

wordNet provides lists of 3 neighbors on average for the 10,473 names of the corpus it covers, while Moby provides lists of 50 neighbors on average for 9,216 names.


DTKs approximate standard tree kernels (TK), that is, DT (t x ), DT (t y ) ≈ T K(t x , t y ), by approximating the corresponding reproducing function (Aronszajn, 1950).

these distributed trees are small vectors that encode structural information.


This is in contrast with distributional semantics vectors where is allowed to be any value in [0, 1] according to the similarity between the words v and u.

early attempts to include distributional vectors in the DTs failed (Zanzotto and Dell'Arciprete, 2011).


We compared systems using the Pearson's correlation as the standard evaluation measure for the challenge 3 .

results can be compared with the results of the challenge.


Comparing rows in the SS columns, we can discover that DST K 0 and DST K +1 behave significantly better than DT K and that DST K 0 behave better than the standard TK.

our DSTKs are positively exploitng distributional semantic information along with structural information.


Domain Selection: |V| are created as follows: for each word w i in our model we create a corresponding domain v i that consists of all the words that are semantically similar to w i , i.e., the ith domain is the semantic neighborhood of word w i .

in our model the vocabulary size is equal to the domain set cardinality, i.e., n = |V|.


Here, a definite NP is marked as orphan if it is still a singleton and it does not contain a personal name.

an orphan NP is linked to the previous PN with gender and number agreement.


This is due to the execution of the two default rules: as the system uses more modules, the amount of (partial)entity mergings (usually) grows.

the precision increases because the new mergings restrict incorrect links performed by the two default rules in the previous models.


5.8 Spurious links between Noun Phrases sharing the same head (1.3%: 0% / 4% / 0%) In the same text, different entities can share their heads in some mentions, which may involve errors in coreference links, depending on their position and on their features.

the NP "the president" may be linked to two different persons like "the president of the Academia" and "the president of the Government".


5.10 Errors due to fixed language structures (0.7%: 2% / 0% / 0%) Other minor errors include some fixed structures such as the following cataphoric possessive:  [...] The results of the error analysis bring interesting information to further work.

including some kind of semantic knowledge (synonyms), improving pronominal coreference resolution and implementing specific rules for quoted speech might solve many of the most frequent errors made by LinkPeople.


be very specific to a particular genre or sub-genre.

provided we have enough examples to get reliable distributional statistics, these larger units are likely to provide useful information for various downstream applications.


3 While most of these are wide-coverage grammars, and are being actually used for morphological disambiguation in Apertium, they are also too big and complex to be easily used for the early stages of parser development.

we have written a small Hungarian CG, aimed to fully disambiguate a short Hungarian story, which was used as the development corpus.


Since VISL CG implements CG-3, and fomacg only supports CG-2, a one-to-one comparison with the grammars above was not feasible.

we extracted the subset of rules from each that compiled under fomacg, and carried out the tests on these subsets.


The custom FSA applier function that implements only the necessary features was employed for both rule testing and finding the delimiter cohort.

running time went down to 1.45 seconds (see table 2), a 75% improvement.


To achieve the speed-up described above, however, the rule checking automata must be determinised, which may cause them to blow up in size exponentially.

building a single tree from all rules is not feasible.


On the other hand, it is very time-consuming and labor-intensive to manually construct treebanks.

lots of recent work has been devoted to get help from bilingual constraints.


However, one potential disadvantage is that bilingual treebanks and bitext with one-side annotation are difficult to obtain.

they usually conduct experiments on treebanks with a few thousand sentences.


For example, as shown in Figure 1(a), the two Chinese words " 2 " and " 5 " are aligned to the two English words "did 3 " and "things 1 ", and the dependency "things 1 did 3 " is included in the source tree.

we project the dependency into the target side and add a dependency " 2 5 " into the projected structure, as shown in Figure 1(b).


Since the number of projected sentences is much more than that of manually labeled instances (0.9M vs. 16K), it is likely that the projected data may overwhelm manually labeled data during training.

we adopt a simple corpus-weighting strategy.


As outlined in Section 3, our feature selection experiments require the training of a large number of parsing models and applying these to the development set.

5 we aimed to find a training setup for the parser that provided fast training times while maintaining a realistic training and optimization scenario.


In order to address the out-of-vocabulary issue, the obligatory case information and the dependency language models were constructed from large text corpora and applied to the selectors.

arguments that could not be analyzed by PASA for newspaper articles (e.g., zero-pronouns of the first and second persons in the nominative case) became analyzable by adding only a small number of dialogues.


To reduce computational cost, we generate for every feature its contextual features based on the same feature template.

the embeddings for each feature template is trained separately.


Previous studies showed that obtaining social support in OHCs can help people feel better (Dunkel-Schetter, 1984;Maloney-Krichmar and Preece, 2005;Beaudoin and Tao, 2007;Vilhauer, 2009;Qiu et al., 2011).

of online interactions in OHCs, a huge volume of user-generated content exists today on various issues/problems related to specific diseases.


For instance, psychiatric retrieval systems are able to retrieve relevant documents according to the depressive problems (emotion labels) described in user queries so that the users can learn self-help knowledge from the responses.

this study aims to identify emotion labels from psychiatric social texts.


The Locality Sensitive Hash function H used by Simhash ensures that where Sim(t 1 , t 2 ) is the cosine similarity between t 1 and t 2 .

it suffices to compute a diversity score on the (much smaller) set of 128-bit fingerprints of the tweets containing the aspect.


In spite of this, we observed reasonable correlation between the propensity of a phrase on Twitter to be a true aspect and the quality of web search result that we can retrieve.

this approach results in generating large noisily labeled datasets, which can often be more effective than a small dataset with high quality labels (Fuxman et al., 2009).


On the other hand, a tweet, besides its local importance indicator, may be more important if it is similar to the theme of the news content.

cross-media correlation features are designed to incorporate the auxiliary information source for helping instance ranking.


Also, the good extracts from different sources may not cover the same set of ground-truth.

maybe we can try to combine the extracts from both sides for further improvement.


The idea is to identify a concise region in feature space that contains (most of) the documents of the author of interest while other documents are considered outliers.

the model can be viewed as a profile-based approach in feature space while the data is treated on an instance-based level.


Density level set estimation, also known as one-class learning (Tax and Duin, 1999;Schölkopf et al., 1999), is the problem of learning a representation of a single class of interest, rejecting data points that deviate from the learned model of normality.

it has been proven very successful in anomaly detection scenarios such as network intrusion detection (Görnitz et al., 2009).


We therefore generalise the semi-supervised anomaly detection of the previous section as a member of the multiple kernel learning framework .

we aim to learn a weighted combination of T kernels with mixing coefficients β = (β 1 , .


By using our MKL, selecting features and or kernel functions is no longer necessary as the learning algorithm itself picks the right combination of kernels for the problem at hand.

the more kernels are thrown into, the richer the decision space for the MKL.


3.1.4 Sentiment Feature We pointed out in Section 1 that authors often use the antithesis figure of speech to express contrasting concepts.

another feature which we can use is the aggregated absolute difference values between the sentiment polarities of words in a text paragraph.


For every background knowledge b extracted automatically from a relevant corpus, the more units are relevant to b and the more important they are, the more relevant b becomes to the source document.

we extract both source document information and background knowledge automatically and present them together in a document-level graph.


Discourse parsing aims to identify the discourse relations held among various discourse units in the text.

one can expect that discourse parsing provides useful information to the evaluation of text coherence, because, essentially, the existence and the distribution of discourse relations are the basis of the coherence in a text.


Nonetheless, long-distance discourse dependency could be quite useful for capturing text coherence from a global point of view.

in this paper, we study the effect of deep hierarchical discourse structure in the evaluation of text coherence, by adopting two approaches to perform a direct comparison between models that incorporate deep hierarchical discourse structures and models with shallow structures.


Moreover, as a baseline, we also re-implemented B&L's entity-based local coherence model, and we will study the effect of incorporating one of our discourse feature sets into this baseline model.

we have four ways to encode discourse relation features, namely, entity-based, PDTB-style, full RST-style, and shallow RST-style.


First, we differentiate between intra-and multi-sentential discourse relations, which is motivated by a finding in the field of RST-style discourse parsing -distributions of various discourse relation types are quite distinct between intra-sentential and multi-sentential instances (Feng and Hirst, 2012b; Joty et al., 2012) -and we assume that a similar phenomenon exists for PDTB-style discourse relations.

we assign two sets of discourse roles to each entity: intra-sentential and multi-sentential roles, which are the roles that the entity plays in the corresponding intra-and multi-sentential relations.


We then generate four separate set of features for the combination of intra-/multi-sentential discourse relation roles, and salient/non-salient entities, among which transitions consisting of only nil symbols are excluded.

the total number of features in PDTB-style encoding is 4 × (36 2 + 36 3 − 2) ≈ 192K.


In this experiment, our learning instances are pairwise ranking preferences between a source text and one of its permutations, where the source text is always considered more coherent than its permutations.

we have 735×20 = 14, 700 total pairwise rankings, and we conduct 5-fold cross-validation on five disjoint subsets.


This is primarily due to the fact that the discourse relations discovered by RST-style parsing have wider coverage of the text 5 , and thus induce richer information about the text.

because of its ability to annotate deep discourse structures and its better coverage of discourse relations, RST-style discourse parsing is generally more powerful than PDTB-style parsing, as far as coherence evaluation is concerned.


As discussed previously, the PDTB-and RST-style discourse parsers used in our experiments are far from perfect.

it is possible that using automatically extracted discourse relations creates some bias to the training procedure; it is also possible that what our model actually learns is the distribution over those discourse relations which automatic discourse parsers are mostly confident with, and thus errors (if any) made on other relations do not matter.